Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

作     者：Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Debasmita Mishra;Bighnaraj Naik;Janmenjoy Nayak;Alireza Souri;Pandit Byomakesha Dash;S.Vimal

作者机构：Department of Computer ApplicationVeer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer ScienceMaharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software EngineeringHaliçUniversity34394IstanbulTurkey Department of Information TechnologyAditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics LabDepartment of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2023年第9卷第1期

页      面：125-137页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection 

摘      要：In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.